{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "4.0\n"
     ]
    }
   ],
   "source": [
    "e = float(input())\n",
    "x = 0\n",
    "n = 1\n",
    "while True:      #不断循环去找出条件符合的结果\n",
    "    x = x + pow(-1,n + 1) * (1/(2 * n - 1))   #格雷戈里公式\n",
    "    if(e > abs(1/(2 * n - 1))):  \n",
    "        break    #满足条件跳出循环\n",
    "    else:\n",
    "        n += 1\n",
    "print(x*4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 函数图像\n",
    "## 1:y=x**2\n",
    "## 2:y=x+3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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35qB3GQt+zrOJg2i69OMlrDl4kl9d2dVlOoMem9yv6BpG59Ag3l+bxaS+kSiZLyyaoqrEKMiVkgwHV0BNmTHE0mOS0UPvfAX4yP0cd/HBOqP6421OPv2xPlu22QsAVgP+1vaLtNbPNWjjD8wHBgIFwC1a62y7R2tHXl6KXwyP49kl+9iWc4qkONmUQFxAxal6hbm+h7oqCAqHvrcYST1uhBTmckOnyqr5z/Z8bugXRagLTcCwpedeBYzRWpcqpXyBtUqpb7TWG+u1mQ6c0lrHK6Vuxdhj9RYHxGtXNw6M5uXlacxdkyXJXTSu7KRRmCs12VqYqxZaRUHSvcaQS8xgKczl5j7ZnEtljYXpl7vWRuC2bLOngVLrt77WL92g2WTgeevjRcAbSillfa3TCvTz4fbBsfzrxwxyC8qJbSfjogI4fcTYdi5lCeSsA22BNnEw5CFjyCVygBTm8hDVtRbmrc/m8oRQurZ3rRvhNo25WzfH3gbEA29qrTc1aBIF5AForWuVUsVAO+CkHWN1iHuGxvHu6kw+WJ/Fc9f1NDscYZaiXGthrmRjPjoaQrvCiF8aCb1Db6nj4oG+2n2Y4yVVLrng0abkrrWuA/oppVoDXyilemmt99Zr0thP/c967UqpGcAMgNhY57gx0SEkgGv7RPD5ljyeuLIrrQJkzNRjFGQYvfPUZDi8wzjWvjeM/q0xhh7e3dz4hKm01ry3NouE8GBGJrjeArMmzZbRWhcppVYB44H6yT0fiAHylVI+QAhQ2Mjr5wBzAJKSkpxmyGb6iM58ufMwn23O4/6Rnc0ORziK1nA89Wwdl+P7jONRA2HcH4wx9Lby7y8MGzML2Xf4NH+Z0tslZ9PZMlsmDKixJvYWwDiMG6b1JQP3ABuAm4CVzj7eXl/v6BAGdWrLh+uzmTY8Dl+p9e4+tIYju6wJfQkUpAMKYofC+L8ay/5bO9/mxsJ8c9dk0jbIjxv6u+Y6GFt67hHAPOu4uxfwudb6K6XUbGCr1joZeA9YoJRKx+ix3+qwiB1kxuWduW/+VpbtkUVNLs9igUNbzw65FOWC8jamKg55ELpfBy3bmx2lcGLpx0v4fv9xHh+XQICva86GsmW2zG6gfyPHf1/vcSUw1b6hNa8x3cPpEhbEnNWZsqjJFVnqIHfD2TouJYfByxc6j4KRv4ZuEyGondlRChcxd00W/j5e3DXEdcste+wK1Ya8vBT3X96ZpxbvYUNGAcPiXe8Gisepq4Gs1UbvfP/XUHYCfAIgfhz0eB66Xg0tWpsdpXAxx0sqWbz9EDdfFu3SVWMluddzff8oXl5+gDlrMiW5O6vaKsj44WxCrywCv2CjIFfiJIi/EvyDzY5SuLD563OosViYPsK1b65Lcq8nwNebe4bG8fcVaRw4WkK3Dq61aMFtVZdD+gprYa7voLoE/EOg2wRrYa4x4NvC7CiFGyivrmXBxhyuSmxPp9Ags8O5JJLcG7hzSEfeXJXOu2syedkFFy64jcrTRiJPXQIH/wu1FRDYDnrdAD0mQ6eR4ONndpTCzfx7az7FFTXMcIMp0ZLcG2gT5MctSTF8sjmXX13VlYgQ6RE2m/JCOPCNMeSSsRLqqiG4PfS/w1hU1HE4eMuPrHCM2joL767JZGDHNgzs6Pq1puR/SiPuu7wzH23K5f21WTxzTaLZ4bi30uPWOi7JkL3GKMwVEgOX3W8MuUQPkjouoll8vecI+acq3KYMiST3RsS0DeTaPhF8simXWaMTCAmUkgR2dfqwtY7LEmP6orYYK0OHzrIW5uovdVxEs9Ja886PmcSHBzO2e7jZ4diFJPdzeGBkF5bsPMxHm3J4eHS82eG4vlPZ1jnoyZC/xTgW1gNG/p8x5NK+pyR0YZof006QeuQ0f7upD15e7vFzKMn9HBIjW3FF1zA+WJfF9BGdXHaVmqlOpBk3RFOS4ehu41hEXxjzrNFDD5XNyYVzeOfHDDq0CuB6N1qdLsn9PGZe0YXb3t3Iom353OnCK9WajdZwbN/ZwlwnUo3jUUlw5R+hx3XQ1rU2PBDub2deERszC3lmYg+X2R/VFpLcz2NI57b0jWnNnNWZ3HpZDD5SUOzntIbD288OuRRmgvKC2GEw/kUjoYe4T29IuJ93VmXQMsDHpfZHtYUk9/NQSvHQqC48sGAbX0tBsbMsFsjffDahF+cZhbk6jYRhjxiVFoPd46aUcG/px0v4dt9RHhkTT7C/e6VD9/rbOMCVPdqTEB7M26syPLugWF2tseVcajKkfgWlR8Hbz1gdOuppY7VooOvPDRae5e1VmbTw9eYXw91vuFCS+wV4eSkeGt2FJz7bxcr9xxnbw4NKxdZWWwtzLTHquJQXgE8LSBgHidcb9VwCWpkdpRAXJa+wnC93HuKeoXG0DXK/1c6S3G1wXZ9I/r48jTd+SGdM93D37r3XVBirQ1OSjdWiVcXg19KosJg4yai46OfaNTeEAHh3TSZeCu4f6X69dpDkbhMfby8euKILz365l42ZhQzt4mZ1watK4eByY8glbTnUlEFAa+h+jZHQO48G3wCzoxTCbo6XVPLpljym9I922xIjtmyzFwPMBzoAFmCO1vrVBm1GAUuALOuhxVrr2fYN1VxTB0bz6n8P8uYP6e6R3CuL4cC3RkJP/y/UVkJgKPSZaiwq6jQSvGVlrnBP76/NprbOwsxRXcwOxWFs6bnXAr/SWm9XSrUEtimlVmitUxq0W6O1vtb+ITqHAF9vZozsxJ+X7WdH7in6x7YxO6SmKy80xs5TlkDmKrDUQMtIGHCP0UOPHQpeslhLuLdTZdUs2JDNxN4RLl/W93xs2WbvCHDE+rhEKZUKRAENk7vbu2NwR95elcHrK9N5f9plZodjm5JjsH+ptTDXWtB10LojDJlplM6NGiiFuYRH+WBdFmXVdcwa495lRZo05q6UisPYT3VTI08PVUrtAg4DT2qt9zXy+hnADIDYWNdbMBDk78P0EZ14eXkaew8V0ysqxOyQGleUZxTmSk2G3I2AhnYJMOJxY9l/hz5Sx0V4pNOVNXywPpvxPTvQvYN7z/SyObkrpYKB/wCPa61PN3h6O9BRa12qlJoIfAn8rHCI1noOMAcgKSlJX3TUJrp7WBxzVmfy+sqD/OuuJLPDOasg4+yy/8PbjWPtexlz0BMnQVh3SejC481bl01JZa3b99rBxuSulPLFSOwfa60XN3y+frLXWi9TSr2llArVWp+0X6jOoVWAL9OGd+K17w+SeuQ0PSJM/O1/fP/ZhH5sj3Essj+Mfc7oobdz35tFQjRVaVUt763LYmz3cOf91G1HtsyWUcB7QKrW+pVztOkAHNNaa6XUIMALKLBrpE7k3uFxvLcmkzd+SOfN2wc034m1hqN7zib0kweM4zGD4eo/G3VcWrvecJcQzeGjjTkUldfwyFjPqEZqS899OHAXsEcptdN67LdALIDW+h3gJuBBpVQtUAHcqrV2yWEXW7QO9OPuYXG882MGB4+VkNDegRtpaw2HthkzXFKTjbroysvYcm7Q/UYdl1YRjju/EG6gvLqWd1dncnlCKP1iWpsdTrOwZbbMWuC8g7Va6zeAN+wVlCu4//LOzFufzavfH+QNe/feLXWQt8ma0JfC6UPg5Qudr4ARvzQWFwWF2vecQrixBRtyKCir5vFxntFrB1mhetHaBvlxj7X3/uixErpeau+9rsaYqnimMFfZcfD2h/ixMPb30HU8tPCMHocQ9lReXcu/rL12d9j42laS3C/B/Zd3Zv76bF672N57bZWxmCglGQ58DRWnwDfQKMiVOMn409+BQz5CeID5G3IoLKvm8XFdzQ6lWUlyvwRneu9vN6X3Xl0OGd8bCT3tW6g6Df6tjJK5PSYZPXVf96x1IURzK6uqZc7qTEZ2DWNgRxdcVX4JJLlfovpj7+ecOVNVAmnfGUMuB1dATTm0aGP0zhOvh05XgI/7lRwVwmxne+2eM9Z+hiT3S9QmyI9pw+N4a1UG+4+ePrvqreKUUZgrZYlRQreuCoLCoe+txhz0jiPAWy6/EI5SWlXLnNUZjOwaxgBXrAV1iSS72IEx9p7Du99u5u+9DxkJPetHsNRCq2hIutfopccMlsJcQjSTD9Zmcaq8hl9d6Vlj7WdIcr9Up4/QOnUpy1p/SmTWdsjW0CYOhj5sLcw1QJb9C9HMistrmLMmk3E92tPXQ+a1NyTJ/WKcyjm7SjR/MwCR7brynppCfsQ4Zs+4TRK6ECZ6d00mJZW1/NJDe+0gyd12J9ONvURTkuGIdaFuh94w+neQOAnvsG7Urcpg/rf7mZx7yqPm0wrhTApKq3h/XRbX9IkgMdK9Kz+ejyT3c9EajqcYyTw12XgMRv3zcX8wxtDbdv7JS+4Z1pH31mby9+VpfHL/EBOCFkL8a3UmlTV1POGBM2Tqk+Ren9ZGrzwl2bgpWpgBKGOHovF/NQpzhUSf8+WBfj48NCqe2V+lsC79JMPjpUSAEM3paHEl89Znc32/KOLDPXsBoCR3iwUObT1bmKsoF5Q3dLrcuCna/Vpo2d7mt7t9cCxz12Tyt2/38+XDw1Ey9i5Es3lt5UEsWvOEB4+1n+GZyd1SBznrrXVclkLJEaMwV5fRMPLXRmGuwIsbMw/w9ebxK7vy60W7+W7fUcb3koqNQjSHrJNlfLYljzsHxxLTNtDscEznOcm9rsaYe56SbGwSXX4SfAIgfpyxqKjr1RBgnwL+U/pH8a8fM3h5eRrjerTHx1v2KBXC0V5ZkYaftxezxnj2WPsZ7p3cayoh8wdrYa5lUFkEfsFnC3PFXwn+wXY/rY+3F09e1Y0HP97O4h2HuDkpxu7nEEKctfdQMUt3Hebh0V0Ia+lvdjhOwZadmGKA+UAHwALM0Vq/2qCNAl4FJgLlwDSt9Xb7h2uD6jKjfktqMqQth+oSo0febaJRmKvLGPANcHgY43t1oE90CP9ckcakvpEE+MrKVCEc5eXlBwhp4cuMkbK15Bm29NxrgV9prbcrpVoC25RSK7TWKfXaTMDYEDsBGAy8bf2zeVSethbmWgIH/wu1FRAYCr2mGD30uJHNXphLKcVvxnfnjrmb+GhjDvdd3vnCLxJCNNmGjAJWHTjBUxO6E9LC1+xwnIYtOzEdAY5YH5copVKBKKB+cp8MzLdurbdRKdVaKRVhfa1jlBcaQy0pycbQS101BHeA/ncaCT12mOmFuYbHh3J5Qiivr0xn6sAYQgLlB08Ie7JYNH/5JpWIkACmDYszOxyn0qTsp5SKA/oDmxo8FQXk1fs+33rM/sk9dyOs+itkrQZdByGxMGiGMeQSfRl4OdfNy6cmdOfa19fy1o/pPD2hh9nhCOFWvt5zhN35xbw8ta8MfTZgc3JXSgUD/wEe11qfbvh0Iy/52QbZSqkZwAyA2NjYJoRZ/10txlz04Y8aCT2yv1PXcekZGcIN/aL4YF029wyNI7K1bMQhhD1U11p46bsDdO/Qkhv6R5kdjtOxqZurlPLFSOwfa60XN9IkH6g/JSQaONywkdZ6jtY6SWudFBYWdjHxGqtFH9kG4553mYqLv7zKWFDxyoo0kyMRwn18simH3MJyfjOhO95ezp8HmtsFk7t1Jsx7QKrW+pVzNEsG7laGIUCxw8bblXKJhF5fdJtApg2L4z/b80k90vBDjxCiqU5X1vDaynSGdm7HqK4X2VF0c7b03IcDdwFjlFI7rV8TlVIzlVIzrW2WAZlAOvAu8JBjwnVdD4+KJ6SFLy98nYpx31kIcbHe+iGDwrJqfjuxh5T4OAdbZsuspfEx9fptNPCwvYJyRyGBvjw6JoHZX6Ww6sAJRncPNzskIVxSXmE576/LYkr/KHpH22dVuTtyrqklbu7OIR3pFBrEC8tSqa2zmB2OEC7pb98dwEvBk1d3MzsUpybJvRn5+Xjx1ITupB8vZeGWvAu/QAjxEztyT7F012FmXN5ZZp5dgCT3ZnZVYnsGdWrLP1ekcbqyxuxwhHAZWmv+9HUqYS39eeAKKTNwIZLcm5lSimevSaSgrJo3V6abHY4QLuOr3UfYlnOKX13ZlSB/9655aA+S3E3QOzqEqQOjeX9dFlkny8wORwinV1Fdx1+/2U/PyFZMlSqrNpHkbpL/G98AM3MbAAAZMElEQVQNP28vXvg61exQhHB6c1Zncqiogueu6ykLlmwkyd0k4S0DmDUmgf+mHmPNwRNmhyOE0zpcVMHbP6ZzTZ8IBnW6uB3SPJEkdxPdOyKOju0Cmb00RaZGCnEOf/1mP1rD0xO6mx2KS5HkbiJ/H2+emdiDg8dLWbAxx+xwhHA6W7ILSd51mAeu6EJ0G9kXtSkkuZvsysT2XJ4Qyisr0jhZWmV2OEI4jdo6C89+uZfIkABmXiGb3TSVJHeTKaV4flJPKmvqePGb/WaHI4TT+HhTLvuPlvDstYkE+snUx6aS5O4EuoQFc++ITvx7Wz7bc0+ZHY4QpjtZWsXflx9gRHwo43t1MDsclyTJ3Uk8OiaB9q38eW7JPuosUjVSeLaXvj1AeXUdz09KlKqPF0mSu5MI8vfhmWsS2XOomIWbc80ORwjT7Mg9xWdb85g+ohPx4S3NDsdlSXJ3Itf1iWBYl3b87dv9cnNVeKTaOgvPfLGXDq0CeGRsgtnhuDRJ7k5EKcXsyb2oqKnjz7JyVXigeRtySDlymueuSyRY6sdcElu22XtfKXVcKbX3HM+PUkoV19ul6ff2D9NzxIcH88DILizecYj1GSfNDkeIZnO0uJJXlh9gVLcwuYlqB7b03D8Exl+gzRqtdT/r1+xLD8uzzRoTT2zbQJ79ci/VtbJyVXiGP36VQq1FM3tSL7mJagcXTO5a69VAYTPEIqwCfL35w+SeZJwoY87qDLPDEcLhVh04ztd7jvDImHhi28lKVHuw15j7UKXULqXUN0qpnnZ6T482uls41/SO4LWV6WSeKDU7HCEcpry6lt99uZf48GDuHykrUe3FHsl9O9BRa90XeB348lwNlVIzlFJblVJbT5yQSogX8tx1ifj7ePHbL/Zg7EEuhPt5ZXka+acq+MuU3vj7eJsdjtu45OSutT6ttS61Pl4G+CqlQs/Rdo7WOklrnRQWFnapp3Z74a0C+O3EHmzMLOTfW/PNDkcIu9uTX8z767K4fXAsl8VJOV97uuTkrpTqoKx3P5RSg6zvWXCp7ysMtyTFMCiuLS8sS+VEicx9F+6jts7CU4t3Exrsz2/GSzlfe7NlKuRCYAPQTSmVr5SarpSaqZSaaW1yE7BXKbULeA24VcsYgt14eSn+PKU3FdV1PL90n9nhCGE3c9dmse/waf4wqSchLXzNDsftXHCVgNb6tgs8/wbwht0iEj8THx7Mo2PjeXl5Gtf1OSpzgIXLyzhRyisr0rgqsb38PDuIrFB1EQ9c0YXEiFY8u2QvReXVZocjxEWrs2h+vWg3LXy9+dP1MqfdUSS5uwhfby9emtqHU2XVzP4qxexwhLho8zdksy3nFL+/NpHwVgFmh+O2JLm7kJ6RITw4qguLtx/ih/3HzQ5HiCbLLSjnb98aJQamDIgyOxy3JsndxcwaE09CeDBPL95DcXmN2eEIYTOLRfN/i3bh7aX48w29ZTjGwSS5uxh/H2/+fnNfTpRWyewZ4VI+WJ/NpqxCfn9dIpGtW5gdjtuT5O6C+kS35uHR8Xyx4xDf7j1idjhCXFD68VL+9u1+xnYPZ+rAaLPD8QiS3F3UrNHx9IxsxTNf7JWNPYRTq62z8Kt/76KFnzd/mSLDMc1FkruL8vPx4pWb+1FSWcszUntGOLG3V2WwK6+IP13fS2bHNCNJ7i6sW4eW/Oqqrny375jUnhFOaVdeEa9+f5Dr+kZybZ9Is8PxKJLcXdx9l3dmSOe2PL90HzkFZWaHI8T/lFfX8vhnOwlv6c+fJvcyOxyPI8ndxXl7KV65uR8+XorHP9tJbZ3s3CScwx+/SiW7oIy/39yPkECpHdPcJLm7gcjWLXjhht7syC3i9ZXpZocjBCtSjrFwcy4zRnZmaJd2ZofjkSS5u4nr+kYypX8Ur688yOYs2RVRmOdocSW/XrSLxIhW/PLKrmaH47EkubuR2df3IrZtII99ukOKiwlT1Fk0j326g6paC6/f3l92VjKRJHc3Euzvw+u3DeBkaRX/t2i3TI8Uze6Nlelsyipk9uRedAkLNjscjybJ3c30jg7hN+O7syLlGAs25pgdjvAgm7MKefX7NG7oH8WNUhTMdLbsxPS+Uuq4UmrvOZ5XSqnXlFLpSqndSqkB9g9TNMX0EZ0Y0z2cP32Vyp78YrPDER6goLSKRxfuILZtIH+UGu1OwZae+4fA+PM8PwFIsH7NAN6+9LDEpVBK8fLUvrQL9uOhT7ZJ9UjhUHUWzeOf7aSwvJo37xhAsP8FN3gTzeCCyV1rvRo43/SLycB8bdgItFZKRdgrQHFx2gb58cbtAzhSVMmTi3bJ+LtwmNdXHmTNwZP8YVJPekaGmB2OsLLHmHsUkFfv+3zrMWGygR3b8PTEHqxIOca7azLNDke4obUHT/Lq9weZ0j+KWy+LMTscUY89kntjg2uNdhOVUjOUUluVUltPnDhhh1OLC7l3eBwTenXgxW8PsCGjwOxwhBs5VFTBo5/uID4smD/dIOPszsYeyT0fqP8rOxo43FhDrfUcrXWS1jopLCzMDqcWF6KU4m839SGuXSCzPtnO4aIKs0MSbqCypo6ZC7ZRU2vhnbsGEugn4+zOxh7JPRm42zprZghQrLWWHSScSMsAX/51VxJVtRYe/GgblTV1ZockXJjWmt99uZc9h4p55ZZ+Mp/dSdkyFXIhsAHoppTKV0pNV0rNVErNtDZZBmQC6cC7wEMOi1ZctPjwYP5+c1925Rfz+yV75QaruGgfbcxh0bZ8Hh2bwJWJ7c0OR5zDBT9Laa1vu8DzGnjYbhEJh7m6ZwceGRPP6yvTSYxoxbThncwOSbiYDRkF/GFpCmO6h/P42ASzwxHnIStUPcwT47pyZWJ7Zn+Vwuo0uaktbJdbUM6DH28jLjSIf97aDy8vuYHqzCS5exgvL8U/bulH1/YtefiT7WScKDU7JOECSiprmD5vCwBz706iVYDUZ3d2ktw9ULC/D+/enYSftxf3z9sqFSTFeRmVHneSdbKMt+4YQFxokNkhCRtIcvdQMW0DeeeugeSfquCBBduoqpUZNOLntNb8Yek+Vu4/zvOTejKsS6jZIQkbSXL3YJfFteWlqX3YlFXIU//ZIzNoxM+8tzaL+RtymDGyM3cO6Wh2OKIJZOWBh5vcL4q8wnJeXp5GTNtA2TlH/M+3e4/ywrJUJvTqwFPju5sdjmgiSe6Ch0fHk1tYzmvfHySqdQC3XBZrdkjCZNtyCnns0x30i2nNP26RmTGuSJK7QCnFCzf05nhJFU8v3kObQD+u6tnB7LCESdKOlXDvh1uJbN2CuXcnEeArW+W5IhlzFwD4envx1h0D6B3dmkcW7pBNtj3UoaIK7n5vM/4+Xsy/dxDtgv3NDklcJEnu4n8C/Xz4YNplRLVpwfR5W0g9ctrskEQzKiit4u73NlFWVcu8ewcR0zbQ7JDEJZDkLn6ibZAf8+8dRLC/D3e9t0kWOXmI4ooa7n5/M/mnKph7TxI9IlqZHZK4RJLcxc9Etwnko/sGA3Dn3E3kFZabHJFwpLKqWu79cAtpx0p4566BDO7czuyQhB1IcheN6hIWzILpgymvruOOuZs4WlxpdkjCASpr6pixYCs784p4/bb+jO4WbnZIwk4kuYtz6hHRinn3DqKwrJrb3t0oCd7NVNbUcf/8razPKOClm/owvpdsfexOJLmL8+oX05p59w7iREmVJHg3ciaxr00/yd9u7MOUAdFmhyTsTJK7uKCBHdv8L8HfOmcDR4plqz5XVlFdx33zjMT+0k19mZokG1u7I5uSu1JqvFLqgFIqXSn1VCPPT1NKnVBK7bR+3Wf/UIWZBnZsw/zpgygorWbqOxvIKSgzOyRxEUoqa7jn/c2syzAS+00DpcfurmzZZs8beBOYACQCtymlEhtp+pnWup/1a66d4xROYEBsGz65fwhlVbVMfWcDB4+VmB2SaIJTZdXcMXcT23NP8dqt/SWxuzlbeu6DgHStdabWuhr4FJjs2LCEs+odHcJnDwwF4OZ/bWB3fpHJEQlbHDtdyS1zNrD/aAn/umsg1/WNNDsk4WC2JPcoIK/e9/nWYw3dqJTarZRapJRqdBBPKTVDKbVVKbX1xAnZ4s1VdW3fkn/PHEqQvw+3ztnIqgPHzQ5JnEf68RKmvLWe/FMVfDjtMsb2kE2tPYEtyb2xcnANC38vBeK01n2A/wLzGnsjrfUcrXWS1jopLCysaZEKp9KxXRCLHxxGXLsg7pu3lUXb8s0OSTRia3YhN769gapaC5/NGMqweNlsw1PYktzzgfo98WjgcP0GWusCrXWV9dt3gYH2CU84s/BWAXz2wBCGdG7Hk//exWvfH5QNP5zIN3uOcMfcTbQN8mPxg8PoHR1idkiiGdmS3LcACUqpTkopP+BWILl+A6VU/dUPk4BU+4UonFnLAF/en3YZU/pH8cqKNB7/bCeVNbJln5m01ryx8iAPfrydnpGt+M+Dw4htJ0XAPM0F67lrrWuVUrOA7wBv4H2t9T6l1Gxgq9Y6GXhUKTUJqAUKgWkOjFk4GT8fL/5+c1+6hAfz0ncHyC0sZ85dSYS1lHKxza2ypo6nF+/hix2HuL5fJH+9sY/UY/dQyqyP0UlJSXrr1q2mnFs4zjd7jvDE5ztpE+jHW3cMoH9sG7ND8hiHiyp48KNt7Mov5smruvLw6HiUkh2U3I1SapvWOulC7WSFqrCrCb0jWDRzGN5eilv+tZGFm3PNDskjrM84yXWvryXjRBnv3DmQWWMSJLF7OEnuwu56RYWwdNYIBnduy9OL9/B//95FeXWt2WG5JYtF8/aqDO6cu4nWgb58+fBwxveSLRKF7KEqHKRNkB8f/mIQ/1iRxpur0tmRV8Qbt/enewfZBMJeTpRU8cvPd7Lm4Emu6R3Bizf1Idhf/ksLg/TchcN4eymevLobC+4dTFF5DZPfWMeCjTkyXdIOVqedYMKra9icVcifb+jNG7f3l8QufkKSu3C4EQmhfPPY5Qzu3I5nv9zLPR9skdLBF6m8upbffbmHu9/fTJtAX5bMGs7tg2NlfF38jCR30SzCWvrz4bTL+OPknmzJKuSqf/zIFzvypRffBFuyC5nw6ho+3pTLfSM6sfSRETLMJc5JkrtoNl5eiruGxrHssctJaN+SJz7bxT0fbCG3QPZoPZ/i8hqeXryHqe9soM6iWXj/EH53baLMXxfnJfPchSnqLJoFG7J56bsD1GnNY2O7Mn1EJ/x8pL9xhtaapbuPMHtpCoVlVUwf0YknruxKoJ+MrXsyW+e5S3IXpjpcVMFzyftYkXKMTqFBPDOxB2N7hHv8GPKe/GJmf7WPLdmn6B0Vwl+m9KZXlNSGEZLchYv54cBx/vhVCpknyrg8IZTfjO/ukcnsUFEF/1yRxqLt+bQN9OPJq7txc1IM3l6e/ctOnCXJXbicmjoL8zfk8Nr3BymuqOGa3hH88qqudAkLNjs0hztZWsVbP2Tw0cYcAO4e2pFHxyXQKsDX5MiEs5HkLlzW6coa5q7OZO7aLCpr6rimTyQzr+hMz0j368kfLqrg3TWZfLo5j6raOm4aGM1j47oS1bqF2aEJJyXJXbi8k6VVvLsmk4835lJaVcuobmHcO7wTI+JD8XLxYYq9h4r5cH02S3YewqJhcr9IHhoVT3y4+39KEZdGkrtwG8XlNSzYmM2H67M5WVpNp9Ag7hzSkSn9o2gT5Gd2eDarqK7ju31Hmb8hm+25RbTw9ebmpGjuH9mZ6DZSb13YRpK7cDtVtXV8s+co8zZksyO3CF9vxehu4UwZEMWobuFOOe+7zqLZnFXIFzvyWbbnKKVVtcS1C+SuoXHcNDCakBYypi6axtbkbtOEWaXUeOBVjM065mqt/9rgeX9gPsb2egXALVrr7KYGLcT5+Pt4c33/KK7vH0XK4dMs3p7PlzsPszzlGIF+3ozqFsZViR0Y2TWMtib26Mura9mYWcB3e4/x39RjFJRVE+TnzYTeEUzpH8WQzu1cflhJOL8L9tyVUt5AGnAlxn6qW4DbtNYp9do8BPTRWs9USt0K3KC1vuV87ys9d2EPtXUW1mcU8N2+oyxPOcaJEmMr38SIVoxICCWpYxv6xrSmfasAh8VQXF7D7kNFbM8pYl3GSXbknqKmThPs78Po7uFcldiesT3CZfGRsAu7DcsopYYCz2utr7Z+/zSA1vov9dp8Z22zQSnlAxwFwvR53lySu7A3i0WzK7+IdeknWZdewLacU1TXWQDo0CqA7hEtiQ8Lpkt4MDFtAukQ4k/7VgG0tGG6YWVNHcdOV3K0uJJDRRVknCgl43gZB46VkHWyDACloFdkCMPi2zG8SyiDO7fF38f5hoqEa7PnsEwUkFfv+3xg8LnaWPdcLQbaASdtC1eIS+flpegf24b+sW2YNSaBypo6Uo6cZmduEbvyi0g7VsqGjAKqai0/eZ2ftxfBAT4E+Xvj7+PNmQGTmjoLpVV1lFbVUFnz09f4eCk6tguka/tgbhoYTb+Y1vSODpF56cJp2JLcGxscbNgjt6UNSqkZwAyA2NhYG04txMUL8PVmQGwbBtTbx9Vi0RwqquBQUcX/euKnymsoraqhrKqOqtq6/7X18fIiyN+HlgE+tArwoX2rADqEBBAR0oKO7QLx9ZY6OMJ52ZLc84GYet9HA4fP0SbfOiwTAhQ2fCOt9RxgDhjDMhcTsBCXwstLEdM2kJi2MvVQuDdbuh5bgASlVCellB9wK5DcoE0ycI/18U3AyvONtwshhHCsC/bcrWPos4DvMKZCvq+13qeUmg1s1VonA+8BC5RS6Rg99lsdGbQQQojzs2lultZ6GbCswbHf13tcCUy1b2hCCCEultwREkIINyTJXQgh3JAkdyGEcEOS3IUQwg1JchdCCDdkWslfpdQJIOciXx6Kc5Y2cNa4wHljk7iaRuJqGneMq6PWOuxCjUxL7pdCKbXVlsI5zc1Z4wLnjU3iahqJq2k8OS4ZlhFCCDckyV0IIdyQqyb3OWYHcA7OGhc4b2wSV9NIXE3jsXG55Ji7EEKI83PVnrsQQojzcInkrpR6SSm1Xym1Wyn1hVKq9TnajVdKHVBKpSulnmqGuKYqpfYppSxKqXPe+VZKZSul9iildiqlHL63YBPiatbrZT1nW6XUCqXUQeufbc7Rrs56vXYqpRqWmLZXLOf9+yul/JVSn1mf36SUinNEHBcR1zSl1Il61+e+ZorrfaXUcaXU3nM8r5RSr1nj3q2UGuAkcY1SShXXu16/b6ydA+KKUUr9oJRKtf5/fKyRNo67Zlprp/8CrgJ8rI9fBF5spI03kAF0BvyAXUCig+PqAXQDVgFJ52mXDYQ24/W6YFxmXC/ref8GPGV9/FRj/5bW50odHMcF//7AQ8A71se3Ap81w/WxJa5pwBvN9fNU77wjgQHA3nM8PxH4BmNntiHAJieJaxTwlQnXKwIYYH3cEkhr5N/SYdfMJXruWuvlWuta67cbMXaDamgQkK61ztRaVwOfApMdHFeq1vqAI89xMWyMq9mvl9VkYJ718Tzg+mY4Z2Ns+fvXj3URMFYp1diWks0dlym01qtpZIe1eiYD87VhI9BaKRXhBHGZQmt9RGu93fq4BEjF2G+6PoddM5dI7g3ci/GbrqHGNvJueCHNooHlSqlt1n1knYFZ16u91voIGD/8QPg52gUopbYqpTYqpRzxC8CWv/9PNn4Hzmz87ki2/rvcaP0Yv0gpFdPI82Zw5v+DQ5VSu5RS3yilejb3ya1Dev2BTQ2ectg1s2mzjuaglPov0KGRp57RWi+xtnkGqAU+buwtGjl2yVOBbInLBsO11oeVUuHACqXUfmtvw8y4HHK94PyxNeFtYq3XrDOwUim1R2udYY/4rOy28bud2XLOpcBCrXWVUmomxqeLMQ6OyxZmXC9bbMdYsl+qlJoIfAkkNNfJlVLBwH+Ax7XWpxs+3chL7HLNnCa5a63Hne95pdQ9wLXAWG0drGrAlo287R6Xje9x2PrncaXUFxgfvS8pudshLodcLzh/bEqpY0qpCK31EevHz+PneI8z1yxTKbUKo9djz+Rut43f7eyCcWmtC+p9+y7GfShn4LCfqUtRP6FqrZcppd5SSoVqrR1ec0Yp5YuR2D/WWi9upInDrplLDMsopcYDvwEmaa3Lz9HMlo28m51SKkgp1fLMY4ybw43e1W9mZl2v+pup3wP87FOGUqqNUsrf+jgUGA6k2DkOZ934/YJxNRiTnYQxlusMkoG7rTNAhgDFZ4bgzKSU6nDmXolSahBG3is4/6vscl6Fsb90qtb6lXM0c9w1a+47yBd51zkdY1xqp/XrzAyGSGBZgzvPaRg9vGeaIa4bMH7zVgHHgO8axoUx62GX9Wufs8RlxvWynrMd8D1w0PpnW+vxJGCu9fEwYI/1mu0Bpjsolp/9/YHZGJ0IgADg39afv81A52a6RheK6y/Wn6VdwA9A92aKayFwBKix/nxNB2YCM63PK+BNa9x7OM8MsmaOa1a967URGNZMcY3AGGLZXS93TWyuayYrVIUQwg25xLCMEEKIppHkLoQQbkiSuxBCuCFJ7kII4YYkuQshhBuS5C6EEG5IkrsQQrghSe5CCOGG/h/Te5YaZOa1+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pylab as pl\n",
    "x = np.linspace(-2, 2, 100)\n",
    "pl.plot(x, x ** 2)\n",
    "pl.plot(x, 0.5 * x + 3)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sin(3) :  0.1411200080598672\n",
      "sin(0) :  0.0\n",
      "sin(math.pi) :  1.2246467991473532e-16\n",
      "sin(math.pi/2) :  1.0\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "print (\"sin(3) : \",  math.sin(3))\n",
    "print( \"sin(0) : \",  math.sin(0))\n",
    "print (\"sin(math.pi) : \",  math.sin(math.pi))\n",
    "print(\"sin(math.pi/2) : \",  math.sin(math.pi/2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:01:20\n"
     ]
    }
   ],
   "source": [
    "# 请输出指定时段的时分秒\n",
    "seconds = 5000\n",
    "h=m=s=0\n",
    "s = seconds % 60\n",
    "m1 = seconds // 60\n",
    "m = m1 % 60\n",
    "m = m1 // 60\n",
    "# 请不要求修改输出语句\n",
    "print(\"%02d:%02d:%02d\"%(h,m,s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 判断只有唯一解 存在情况△=b²-4ac=0# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x3=-1.000\n"
     ]
    }
   ],
   "source": [
    "#求一元二次方程的解\n",
    "import math\n",
    "def equation(a,b,c):\n",
    "    h=b*b-4*a*c  #一元二次方程的解\n",
    "    if h>0:\n",
    "        x1=(-b+math.sqrt(h))/2*a  #sqrt函数求平方根\n",
    "        x2=(-b-math.sqrt(h))/2*a\n",
    "        print('x1=%.3f'%x1,'x2=%.3f'%x2)\n",
    "    elif h==0:\n",
    "        x3=-b/2*a\n",
    "        print('x3=%.3f'%x3)\n",
    "    else:\n",
    "        print('方程无解')\n",
    "equation(1,2,1)"
   ]
  },
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   "outputs": [],
   "source": []
  }
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